operator-probconserv

Learning in infinite dimension with neural operators.

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics

A Library for Advanced Neural PDE Solvers.

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

OOD Generalization and Detection (ACL 2020)

Learning function operators with neural networks.

[ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values

Discovering Conservation Laws using Optimal Transport and Manifold Learning

Rheology-informed Machine Learning Projects